No Free Checker: A Survey of Verifiers for Robot Policies
Abstract
The survey examines robot policy verifiers across availability and credibility, finding that increased accessibility reduces trustworthiness, and proposes metrics to validate verifiers.
A verifier for robot policies reads a candidate behavior and returns a score for how well it did, used both to evaluate vision-language-action policies and to train them. Verifiers range from success detectors and reward models to runtime monitors, safety filters, and temporal-logic specifications. We survey roughly 150 verifiers and compare them along two properties. Availability is how much a verdict costs, how early in a rollout the verdict arrives, and how often a verdict can be asked for. Availability rises as verdicts get cheaper, earlier, and denser. Credibility is how much a high score tells us about the task. Credibility falls as the judgment becomes gameable and self-serving. We group the verifiers by who supplies the judgment: human verifiers, rule-based and formal verifiers, learned and pretrained verifiers, and model-intrinsic verifiers. Across the four families, we find that credibility falls as availability rises. Regardless of who supplies the judgment, there is no free checker. We then examine what validates a verifier itself, and how much a high score tells us. Three measures appear in the literature: agreement with human labels, the performance of the policy it trains, and behavior under reward hacking. We close with nine metrics that make a verifier claim checkable, and coordinates for the verifiers still to be built.
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TL;DR Robot learning now runs on verifier scores. A score decides which demos are kept, which action is executed, and what the policy is rewarded for. We read ~150 verifiers and put them on two axes.
- Availability: what a verdict costs, how early in a rollout it arrives, and how often you can ask for one.
- Credibility: how much a high score tells you about the task.
Grouped by who supplies the judgment (human, rule-based and formal, learned and pretrained, model-intrinsic), credibility falls as availability rises. Regardless of who judges, there is no free checker.
We also ask what validates a verifier itself. Three measures appear in the literature: agreement with human labels, the performance of the policy it trains, and behavior under reward hacking. Every agreement rate and downstream gain we collected was measured on candidates that were never adversarially searched, which is exactly the regime a training reward and a runtime gate live in.
The survey closes with nine metrics that make a verifier claim comparable across papers, six for a score used to select and three for a score optimized against.
📋 Paper list, kept updated: https://github.com/ZJUSCL/Awesome-Robot-Verifier
Missing work, wrong placement, or a metric you think is unreportable in practice: issues and PRs welcome.
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